{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"./../..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# example adapted from: http://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py\n",
    "%reload_ext yellowbrick\n",
    "from yellowbrick.classifier import DecisionViz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"QDA\"]\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    GaussianProcessClassifier(1.0 * RBF(1.0), warm_start=True),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
    "                           random_state=1, n_clusters_per_class=1)\n",
    "rng = np.random.RandomState(2)\n",
    "X += 2 * rng.uniform(size=X.shape)\n",
    "linearly_separable = (X, y)\n",
    "\n",
    "data_set = make_moons(noise=0.3, random_state=0)\n",
    "\n",
    "X, y = data_set\n",
    "X = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L3r3885Pm1QR3u5p20hyMv4OEf86g8T1fza9jJRpfaWgK8Ni/V3LJ8YfQu5sNrV7Z3dRM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\nY4wxLf4/Cq0nQ8e+ZbAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103200278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = classifiers[0]\n",
    "title = names[0]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ALcAR0bKZZjbF3deFHaOIlKPSmGeQqXzUFJYC3wIejDs+EFji7h8CmNkM4CvAo+GGJ+ko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\niIg0+/93XsT9n54NXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108cd70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = classifiers[1]\n",
    "title = names[1]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[2]\n",
    "title = names[2]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[3]\n",
    "title = names[3]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[4]\n",
    "title = names[4]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[5]\n",
    "title = names[5]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature Alpha', 'Feature Beta'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[6]\n",
    "title = names[6]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[7]\n",
    "title = names[7]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[8]\n",
    "title = names[8]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = classifiers[9]\n",
    "title = names[9]\n",
    "    \n",
    "viz = DecisionViz(model, title=title, features=['Feature One', 'Feature Two'], classes=['Class A', 'Class B'])\n",
    "viz.fit(X_train, y_train)\n",
    "viz.draw(X_test, y_test)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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